Different profile of transcriptome between wheat Yunong

bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Different profile of transcriptome between wheat Yunong 201 and its
high-yield mutant Yunong 3114
Feng Chen1,2,3*, Zhongdong Dong1,2, Zhang Ning1, Zhang Xiangfen1, Dangqun
Cui1,2,3*
1
Agronomy College, Henan Agricultural University, Zhengzhou 450002, China
2
Collaborative Innovation Center of Henan Grain Crops, Zhengzhou 450002, China
3
National Key Laboratory of Wheat and Maize Crop Science, Zhengzhou 450002
Corresponding authors:
Feng Chen
Agronomy College / Henan Agricultural University
Zhengzhou (450002), China
Phone: +86-371-63558537
Fax: +86-371-63558122
Email: [email protected]
Dangqun Cui
Agronomy College / Henan Agricultural University
Zhengzhou (450002), China
Phone: +86-371-63558537
Fax: +86-371-63558122
Email: [email protected]
Running title: Trancriptome analysis of wheat Yunong 3114
Keywords: wheat transcriptome, sequencing, gene expression, gene orthology (GO)
annotation, alternative splicing
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Abstract
Wheat is one of the most important crops in the world. With the exponentially
increasing population and the need for ever increased food and feed production, an
increased yield of wheat grain (as well as rice, maize and other grains) will be critical.
Modern technologies are utilized to assist breeding programs. Such as the
transcriptome sequencing, which greatly improves our genetic understanding,
provides a platform for functional genomics research on crops. Herein, to get an
overview of transcriptome characteristics of Yunong 3114, which is screened from the
EMS mutagenized population of, a high quality Chinese winter noodle wheat, due to
its different plant architecture as well as larger kernel size and higher grain weight, a
high-throughput RNA sequencing based on next generation sequencing technology
(Illumina) were performed. These unigenes were annotated by Blastx alignment
against the NCBI non-redundant (nr), Clusters of orthologous groups (COG), gene
orthology (GO), and the Kyoto Encyclopedia of Genesand Genomes (KEGG)
databases. The 90.96% of the unigenes matched with protein in the NCBI nr database.
Functional analysis identified that changes in several GO categories, including
recognition of pollen, apoptotic process, defense response, receptor activity, protein
kinase activity, DNA integration and so forth, played crucial roles in the high-yield
characteristics of the mutant. Real-time PCR analysis revealed that the recognition of
pollen related gene GsSRK is significantly up-regulated in Yunong 3114. In addition,
alternative splicing (AS) analysis results indicated that mutation influence AS ratio,
especially the retained introns, including the pollen related genes. Furthermore, the
digital gene expression spectrum (DGE) profiling data provides comprehensive
information at the transcriptional level that facilitates our understanding of the
molecular mechanisms of various physiological aspects including development and
high-yield of wheat. Together, these studies substantially increase our knowledge of
potential genes and pathways for the genetic improvement of wheat and provide new
insights into the yield and breeding strategies.
Introduction
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Wheat (Triticum aestivum L.) is one of the world’s most important food staple sources,
which nowadays is grown a broad range of climates throughout the world. To the best
of our knowledge, wheat was domesticated to provide calories and protein for
human-kind from approximately 10,000 years ago and has been adapted and ranked
with rice and maize as the world’s most cultivated crop plants [1]. In the year of 2011,
the total world production of wheat reached approximately 702×106 metric tons, with
rice ~ 723×106 tons and maize ~ 885×106 tons [2]. To support the several billion
people living on the earth, the production of high-quality food must increase with
reduced inputs. Many modern biotechnologies are exploited to assist wheat breeding
programs to increase crop yield, as well as nutritional content, drought and salinity
tolerance and biotic tolerance [3-6]. Grain number, panicle number, and grain weight
are three important components of grain yield of wheat. Grain weight is largely
influenced by grain size, which is determined by its three dimensions (length, width,
and thickness) and the degree of filling [7]. Improvement of these factors plays
a pivotal role in further yield increase in wheat-breeding programs.
Molecular understanding of the biology of the developing grain and the knowledge of
the full genome sequence of wheat will undeniably highly assist the wheat
improvement of yield and quality traits [4, 8]. High quality transcriptomics analysis
method increased this understanding. For example, wheat transcriptome sequencing
can be performed to identify candidate genes for a certain phenotype and to develop
SNP markers for tracking favourable alleles in breeding programs [9, 10]. Knowledge
of the transcriptome also provides great aids for the design of microarrays and the
interpretation of RNA-Seq experiments [11, 12].
Assembling the sequence of the transcriptome, in conjunction with the genome
sequence analysis, will promote us to gain insight into the plant’s capacity for
high-level transient gene expression, generation of mobile gene silencing signals, and
hyper-susceptibility to viral infection and environment [13-16]. The difficulty of
sequence assembly is the most significant factor explaining the relatively small
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
number of plant species with finished genome sequences. Plant genomes tend to be
large and highly repetitive; they have a propensity to contain large gene families and
are frequently polyploid. For example, wheat is allohexaploid with a large genome,
estimated at 17 Gb roughly 5 times the size of the human genome [17, 18].
Additionally, the wheat genome is estimated to be 90% repetitive [19], which is a
significantly higher percentage than Brachypodium (22%), rice (26%), and
sorghum (54%) [20]. Thus, the large size and the complex polyploid nature of the
wheat genome make genetic and functional analysis extremely challenging.
To assemble such large and complex genomes, exceedingly efficient computational
procedure with both time and memory resources is essential, simultaneously highly
accurate to avoid mis-assembly of closely related sequences is also a requirement [4].
Recently, de novo transcript assembly analysis has been used to comprehensively
analyze transcriptomes. Several recent studies were performed using massively
parallel sequencing technology, to detail the transcriptome sequencing of various
non-model species, including wheat [21, 22]. De novo assembly of short sequences
of transcripts enables researchers to reconstruct the sequences of entire transcriptomes,
identify and catalogue all of the expressed genes, separate isoforms, and capture
transcript expression levels [23].
The Chinese winter wheat cultivar Yunong 201, showing outstanding dry white
noodle quality and released in 2006 as a high-quality noodle wheat cultivar. In this
study, we got a high yield cultivar line Yunong 3114 derived from Yunong 201, with a
significant increase in grain length, thousand seed weight and plot yield. To identify
putative effector genes, transcriptome sequencing (RNA-Seq) is now being adopted to
analyze the transcriptomes of Yunong 201 and Yunong 3114, in conjunction with
Trinity for assembling. Based on our transcriptome data, a digital gene expression
(DGE) system was used to compare the gene expression profiles of Yunong 3114 and
201. The unigenes were then annotated to putative functions, classifications or
pathways by sequence similarity analysis against the sequences in the public database
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
resources. Of these, 89,644 unigenes abundance were observed which were
categorised into 3 terms in GO database, listing as biological process, cellular
components and molecular function. Using a 2-fold cutoff, we identified a large
changes in enriched expression unigenes relating to primary metabolic pathways,
DNA synthesis, ATP binding, recognition of pollen, apoptotic process, defense
response and so forth. Alternative splicing (AS) play important roles in increasing
protein variation and complexity, function diversities and regulating gene expression
[24]. We also analyzed the AS condition in Yunong 3114 and Yunong 201 to confirm
if mutation may change AS ratio. The results will allow us to reveal the molecular
mechanism underlying the different yield phenotypes of different genotypes, and may
facilitate better understanding the pathways and genes associated with yield
improvement of wheat.
Materials and Methods
Plant growth and treatment
A Chinese winter wheat cultivar Yunong 201, showing outstanding dry white noodle
quality and released in 2006 as a high-quality noodle wheat cultivar, was treated by
EMS (ethyl methanesulfonate). A M2 line was screened from the 0.8% EMS
mutagenized population of Yunong 201 due to its different plant architecture as well
as larger kernel size and higher grain weight, and was self-crossed for four times into
a M6 line Yunong 3114. The high-yield Yunong 3114 showed a significant increase in
grain length (7.5 cm / 10 kernels) and thousand seed weight (45.1g), whereas 10-grain
length and thousand seed weight of wild-type Yunong 201 are 6.4 cm and 39.7g,
respectively. Yunong 201 and its derived line Yunong 3114 were planted and grown at
the Zhengzhou Scientific Research and Education Center of Henan Agricultural
University during 2011-12 cropping seasons under non-stressed conditions.
The flowers and spikes samples were collected from both lines at 7 days, 14 days, 21
days, 28 days and 35 days after flowering, and mature seeds (deposit 1 year),
respectively, and used for generation of small RNA libraries of Yunong 201 and
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Yunong 3114.
Transcriptome Sequencing and Quality filtering
The Solexa RNA paired-end sequencing was performed to generate a number of reads
for transcriptome sequencing, with sequencing performed by commercial service
provider (Oebiotech, Shanghai, China).
Quality trimming of raw Solexa reads was performed using custom Perl scripts
according to the following prescription: a) the sliding window method to remove low
quality: the quality threshold 20 (e 1% -O 5 bp, length was < 35 bp; b) removal in
sequence containing N parts: reads were discarded if the length was <35 bp. The
assembly was done using standard settings.
De novo assembly
To generate a non-redundant set of transcripts, we performed a de novo assembly with
effective wheat solexa sequencing reads, using the software Trinity (ver.
trinityrnaseq_r2012-10-05, paired-end assembly method). Trinity, a short reads
assembling program developed specifically for de novo transcriptome assembly
from short-read RNA-Seq data.
The relatively long-read sequence data were obtained with the 454 sequencing system.
In addition, contig construction is greatly affected by sequence read quality (i.e.,
length) and quantity. The sequence obtained by the above two kinds of different
sequencing methods were mixed: a) using the software AMOS-3.1.0 minimus2
module, with 454 joining together the results for your reference; b) After assembly
and before annotating the transcripts, CD-hit (version 4.6.1) was used to determine
whether there was any redundancy in the final data set. The similarity is 95%, and the
longest sequence of each category is designated as Unigene.
The RNA-Seq reads from each library were aligned to the cDNAs, and the transcripts
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
were assembled using Bowtie (ver. 0.12.8, single-end mapping method), with the
parameters (-v 3 -a --phred64-quals). Expression levels of all unique transcripts
mapped onto the full-length cDNAs and contigs were quantified using the RPKM
values conducting a correlation analysis of the replicates. The RPKM value of each
transcript was calculated using uniquely-aligned reads according to the RPKM
formula:
Differentially expressed transcripts were detected using a G-test with an FDR value
cut-off < 0.01.
Transcripts Annotation
Unigene sequences were analyzed by Blastx alignment search (e-value < 1e-5) against
protein databases NR, Swiss-Prot, TrEMBL, CDD, PFAM, KEGG and KOG retrieved
proteins with the highest sequence similarity (similarity> 30%) for protein functional
annotation and classification. In total, almost 90.96% sequences received annotation
in this way.
For the gene annotation and functional classification of the reference transcripts set
created by pooling raw reads was analyzed by gene ontology (GO) analysis
(http://www.geneontology.org/) to understand the distribution of gene functions at the
macro level. GO and KEGG functional enrichment analysis were implemented to
analyze the transcripts differentially expressed in both wild and mutant wheat types,
with differences transcripts being as prospect, while all transcript being as background.
Hypergeometric distribution algorithm (phyper) was performed to calculate the
prospect transcripts and the P-values of a particular branch of the GO/Pathway
classification. Additionally, Fisher’s exact test with false discovery rate (FDR) was
used to correct and obtain an adjusted P-value.
Real-time quantitative reverse transcription PCR
Total RNA of Yunong 201 and Yunong 3114 were extracted from seedlings and seeds,
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
respectively, according to the method of Chen et al. [70] with a slight modification for
the extraction buffer containing 0.5 mM Tris-HCl, 0.2 mM NaCl, 1% SDS (w/v), 0.02
mM EDTA, 0.01 mM DTT and 0.07% DEPC- H2O (v/v). DNA was removed by
digestion with DNAse I (Qiagen, China) before reverse transcription. cDNA first
strand was synthesized using M-MLV transcriptase (Invitrogen). Two Steps
PrimeScriptTM RT Reagent Kit with gDNA Eraser (Perfect Real Time; TaKaRa) were
used for the reverse transcriptase (RT) reactions. The temperature program was
adjusted as follows: 2 min at 42 °C, 15 min at 37 °C, 5 s at 85 °C, and then 4 °C.
Three biological replicates were performed for each candidate unigene. Specific
primers in selected unigenes were designed by software Primer Premier 5.0 and were
confirmed by directly sequencing PCR products amplified with the specific primers in
Table 1. Amplification with β-actin primers [70] was used as an internal control to
normalize all data. The relative quantification method (2ΔΔCT) was used to evaluate
quantitative variation between the three replicates.
Alternative splicing analysis
Alternative splicing events and alternatively spliced genes were identified by using
BLAST aligning against alternative splicing database, including a common wheat
genome sequencing results in NCBI, Chinese spring sequencing results by UK
(http://www.cerealsdb.uk.net/cerealgenomics/WheatBP/Documents/DOC_WheatBP.p
hp), Chinese spring sequencing results by French INRA and genome A and D
sequencing results reported by Nature [9, 25].
Results
Sequencing and assembling of the wheat transcriptomes
Solexa sequencing was performed to obtain transcriptome expressions. Wheat
mRNAs from wild type and mutant libraries was sequenced using: a) short-read
Illumina solexa technology, and b) long-read 454 GSFLX technology. After quality
checks, excluding low quality reads and trimming of adapters and sizes election, 351
million solexa reads (the average length of 90.57 bp) in total were obtained. Trinity
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
was explored for sequence assembly in this experiment.
Each contig included various transcript isoforms, which cause redundancy. To avoid
difficulties in a quantitative analysis, the redundant contigs would be removed, by
limiting number of contigs and meaning contig length. Thus a non-redundant set of
contigs were generated for the next step of assembly program. After removal of
redundant, the largest assembly, comprising 14,748 transcripts with an average length
of 936.45 bp long and N50 of 1,516 bp (i.e. 50% of the total assembled sequence was
present in contigs of this length or longer), was produced by Trinity. The longest
transcript of each cluster was definite as Unigene. And 289,681 Unigenes with a mean
length of 596.46 bp and N50 of 886 bp were obtained. Finally, we got solexa
sequencing assembled transcripts 74,086 sequences, and 454 sequencing assembled
transcripts 88,332 sequences (Table 1). We combined the two libraries of sequences
for reassembling. The transcripts decreased into 768,141 sequences with average
length of 918.74 bp and N50 of 1,498 bp, Unigene of 422,144 sequences with average
length of 764.49 bp and N50 of 1267 bp (Table 2).
The transcripts and Unigene expression levels were calculated by the RPKM
distribution statistics method (Reads Per kilobase of exon model per Million mapped
reads). The sample reads and transcribed sequence were highly correlated, more than
78% of the reads aligned to the transcription assembled sequences. A total of 414,330
Unigenes were expressed, ratio of 98.15%. The distribution of expression values of
Unigenes in all the samples existed differences (Max, sd, etc.) (Table 3 and Figure 1).
Functional annotation of differentially expressed genes
To understand the functions of differentially expressed genes, all the Unigenes were
aligned by Blastx to the NCBI non-redundant protein database (NR) and the
Swiss-Prot, TrEMBL, Cdd, pfam and KOG protein database, using a cut-off E-value
of 1e-5.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
As for annotation of species distribution, 90.96% of the Unigenes were mainly
annotated on the 10 species as showing in Figure 2. 44% of the Unigenes had top
matches with sequences from barley (Hordeum vulgare), 7% and 3% from rice (Oryza
sativa), 4% form wheat (Triticum aestivum), 4% from sorghum bicolor, 6%, 23% and
the rest from others, respectively.
To further evaluate the completeness of this transcriptome library and the
effectiveness of the proposed annotation process, we searched the annotated
sequences for the genes involved in Clusters of orthologous groups for eukaryotic
complete genomes (COG) classifications. Of the 142,575 matches to the nr database,
29,721 sequences have a COG classification (Figure 3). Among the 25 COG
categories, the cluster for “signal transduction mechanisms” represents the largest
group (6,827, 1.62%), followed by “posttranslational modification, protein turnover,
chaperones” (5,290, 1.25%) and “general function prediction only” (4,727, 1.12%).
The following categories represent the smallest groups: “cell motility” (5, 0.001%),
“Extracellular structures” (80, 0.019%), and “Nuclear structures” (108, 0.026%)
(Figure 3).
Gene ontology (GO) assignments analysis was performed to annotate and classify the
gene functions of Unigenes. 89,644 Unigenes were annotated by GO analysis, and all
of the annotated Unigenes can be categorized into 64 functional groups (Figure 4).
The most abundant categories of GO classification were involved in biological
process, cellular components and molecular function, with “cellular process and
metabolic processes”, “cell and cell part”, and “binding and catalytic activity” terms
gathering the dominant genes in wheat, respectively.
To characterize the complex biological behaviors for the transcriptome, KEGG
(Kyoto Encyclopedia of Genes and Genomes) database was used analyze the pathway
annotation of unigenes. In total, 43,501 sequences were assigned to 313 KEGG
pathways (Table 4). The pathways with most representation by the unigenes were
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Metabolism/Carbohydrate
Metabolism
pathway
(1,855),
Metabolism/Energy
Metabolism pathway (1,767), Genetic Information Processing/Translation pathway
(1,309), Genetic Information Processing/Transcription pathway (1,165), Organismal
Systems/Environmental Adaptation pathway (1,130).
The difference of gene expression between Yunong 3114 and Yunong 201
Samples clustering showed similar gene expression pattern at different growth stages
in Yunong 3114 and Yunong 201 (Figure 5 and Figure S1). As these data showed a
large number of genes highly expressed in the initial stage of wheat flowering both in
Yunong 3114 and Yunong 201. In contrast, a few clusters showed a little difference
(Figure S2).We hypothesized that the treatment of EMS could give birth to some
mutant genes or abnormal expression genes, playing a part in the whole lifelong of the
plant, which probably resulting in high-yield of Yunong 3114 strain. Therefore, we
screened the enrichment GO terms and KEGG pathways for differently expressed
genes and pathways in all the six samples of Yunong 3114. The absolute value of
“|log2 Ratio|>1” was used as the threshold to identify and compare differentially
expressed genes (DEGs) in and between genotypes. Based on the log base 2
fold change values of genes analysis, we condensed and compressed the genes by
removing categories that did not show a significantly different change and displaying
the categories that did show significant change using a false color heat-map-like
display to show up- or down-regulated classes. We identified sixteen enriched specific
functional GO categories represented changes in Yunong 3114 compared with Yunong
201: receptor activity, nuclease activity, defense response, recognition of pollen,
RNA-directed DNA polymerase activity, endonuclease activity, DNA integration,
nucleotidyltransferase activity, transferase activity, protein serine/threonine kinase
activity, protein phosphorylation, protein kinase activity, apoptotic process, ATP
binding, nucleotide binding and RNA-dependent DNA replication (Table 5). These
annotations provided a valuable clue for investigating specific processes, especially
those involved in high-yield.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Recognition of pollen
Pollen, containing the microgametophytes of seed plants, can be recognized by the
stigma of an incompatible plant pistils, afterwards pollination and germination to
initiate double fertilization. The key pollen factor is a small cysteine-rich protein that
interacts directly with the receptor domain of a stigma receptor serine/threonine
kinase to initiate haplotype-specific pollen recognition and rejection. We detected 380
unigenes differently expressed in the category of recognition of pollen, of which 94
unigenes significantly up-regulated and 63 down-regulated (Figure 6). Among these
69 of the up-regualted genes and 54 of the down-regulated genes possess annotations
in Swissprot database, with encoding proteins 19 and 22 respectively. Most of them
encode receptor-like protein kinase (RLK) family proteins, especially a G-type lectin
S-receptor-like serine/threonine protein kinase (GsSRK). In addition, in our data, it is
interesting to found out that 9 from the sharing 11 proteins were RLK proteins, which
were encoded by 41 and 33 unigenes, presented in up-regulated and down-regulated
categories respectively (Figure 6).
A set of nulli-tetrasomic lines and ditelosomic aneuploids available in the Chinese
Spring background were evaluated for physical mapping of 157 up/down-regulated
unigenes using PCR and the fragment-specific primers. Results indicated that 10 out
of 157 unigenes were located on the chromosome 2AL, 4 of them on 2BL and 10 of
them on the 2DL. Relative expression levels of 16 RLK-related unigenes were further
confirmed by real-time PCR. Results indicated that 7 and 9 of them were up-regulated
and down-regulated in Yunong 3114, which is generally consistent with results
analyzed by RNA-Seq.
Apoptotic process and defense response
Similar with apoptosis of animals, plants undergo programmed cell death (PCD),
which plays an important role in the process of development [26], germination of
cereal grains and defense against pathogens [27]. PCD is triggered to restrict pathogen
growth when a plant disease resistance (R) protein recognizes a corresponding
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
pathogen virulence protein. Our results showed 699 unigenes up-regulated in the
enriched GO term of apoptotic process, and 740 unigenes up-regulated in defense
response category (Figure 7 upper). As showed in Figure 7, 539 genes in PCD and
465 genes in defense response were non-annotated. While among the genes with
annotations, 137 genes are shared between PCD and defense response. In addition, we
found few genes in the category of PCD directly encode apoptotic related proteins,
instead of a high percentage of genes in the main accumulation profile encoding
proteins that belong to disease resistant (R) proteins family (Figure 7 lower). Our
results showed that 25 R proteins presented up-regulated. These findings indicate that
the differently expressed R genes may play important roles in high-yield phenotype of
Yunong 3114.
Molecular function and other metabolism processes
As mentioned above, of the 89,644 Unigenes, we also found a large amount genes
enriched in ATP binding, DNA synthesis, kinase activity and etc. (Table 5) which is
essential for providing energy for nutrient synthesis and accumulation. It is worth
noting that the zeatin biosynthesis pathway, which synthesized a class of
phytohormones involved in various processes of growth and development in plants,
presented up-regulated in all the stages of strain Yunong 3114, suggesting an
underlying relationship with high-yield of Yunong 3114. In addition, we also detected
the homologous genes of the known large grain genes in rice. 15 unigenes encoding
F-box/Kelch-repeat proteins differently expressed in Yunong 3114, which probably
benefit for increasing yield.
Alternative splicing in wheat strains Yunong 201 and Yunong 3114
Alternative splicing (AS) serves as an important regulatory mechanism in regulating
gene expression during development and undergoing different environmental pressure,
contributing to the generation of proteomic and functional complexity in higher
organisms [24, 28-30]. To identify potential AS events, we carried out computational
analyses, mapping the assembled expressed sequence tags to the genome predicted
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
gene regions, to determine all potential AS events. In our results, we identified 11521
and 13331 alternative splicing events based on A genome in wheat Yunong 201 and
Yunong 3114 respectively (Table 6). Our study identified that retention of introns is
the major type of alternative splicing events (>77%) in both strains. By comparing the
AS ratio of wild strain Yunong 201 with mutant strain Yunong 3114, we found that the
retained introns (RI, a type of AS) ratio is a little higher in Yunong 3114 with the
ratios of other AS types reduced, which indicates the mutation influence AS. Then, we
analyzed AS in GsSRK and RLK genes, however, the alternative exon of RLK gene is
deferent in wild type and mutant strain, 22276-22358 in Yunong 201, while
22836-22911 in Yunong 3114 (Table 7). We also identified a gene related to
recognition of pollen undergoing AS, however, it failed mapping to the Swissprot
database. Our results suggest that mutation affected AS, which may play an important
role in regulating wheat yield. During the analysis, it is found that AS based on D
genome is significant less than AS based on A genome, which may due to the smaller
size of wheat D chromosome [31].
Discussion
Transcriptome sequencing and GO annotaion
To the best of our knowledge, transcriptomics is a powerful tool, with larger breadth
of the discovery potential over microarrays, to analyze gene expression, single
nucleotide polymorphism (SNP) surveys, quantitative trait loci (QTL) studies,
genomic scans of diversity, and so forth [32-34]. In order to get a better knowledge of
the whole genomic information and the underlying mechanism of high-yield of wheat
as well as analysis the relative genes, in the present study we performs the first de
novo transcriptome sequencing of mixed stages of the new high-yield mutant wheat
strain Yunong 3114 by comparing with common strain Yunong 201. The
characterization of the wheat transcriptome can help elucidate the molecular
mechanisms underlying high-yield of Yunong 3114. Using the Solexa or Roche/454
massively parallel pyrosequencing technology, we have generated 351 million solexa
reads. After filtering, these transcripts were de novo assembled and extensively
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
annotated. 422,144 Unigenes were obtained, of which 89,644 Unigenes were
annotated to 3 terms in the GO database and mapped to 313 KEGG pathways. These
results represent the significant expansion of the wheat transcript catalog and provide
a comprehensive material basis for future functional and expression analysis of genes
of interest.
Gene ontology enrichment analysis is a useful tool to investigate specific processes,
unique functions, and pathways in transcriptome research [35]. Based on this
consideration, we analyzed the statistically significant enrichment of specific GO
terms in the transcripts. In addition, we hypothesized that the mutants caused by EMS
functions all the lifelong of Yunong 3114. And the genes accumulated in GO terms
with continuing functions or differently expressed may contribute to high-yield of
Yunong 3114. Our transcriptome data showed that the differentially expressed genes
with consistent expression patterns in both mutant and common wheat strains.
However, we found the basal differently expressed genes accumulate in several GO
categories, such as regulation of pollen, apoptotic process, defense response, DNA
integrate and so forth. Moreover, among the transcripts, GOs associated with ATP
binding, nucleotide binding, protein phosphorylation, receptor activity, protein kinase
activity and endonuclease activity were significant up-regulated. Plant receptors
function in the recognition and transduction of pathogen signals that give rise to
reprogramming of cellular metabolism. Endonucleases play a key role in replication,
restriction, repair and recombination, as well as in nucleus destruction of PCD in all
organisms. It is reported that the functions of various R proteins also required
phosphorylation, protein degradation, or specific localization within the host cell [36].
These enrichment GO terms will drive some interesting analysis in the future.
Therefore, we focused on several sets of genes in the interesting GO terms compared
with the whole transcriptome background to identify genes involved in understanding
the gene expression changes in high-yield mutant strain Yunong 3114.
As we known, the key factors involved in high-yield are including grain number,
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
grain size. Several genes’ functions in regulation grain size have been identified in
rice. And the complete sequence of the rice genome has provided an extremely useful
parallel road map for genetic and genomics studies in wheat. It is demonstrated that
the rice genome possess a major grain length quantitative trait locus (QTL), qGL3,
which encodes a putative protein phosphatase with Kelch-like repeat domain
(OsPPKL1), which is essential for the OsPPKL1 biological function. OsPPKL1
together with its homolog OsPPKL3 are function as negative regulators of grain
length, whereas homolog OsPPKL2 as a positive regulator [37]. Another study
demonstrated that two novel major genes named GS2 and GS3 involved in the
regulation of grain width and length in rice, GW2 for grain width [38], GS3 for grain
length [39]. In the present study, we also detected 15 unigenes encoding
F-box/Kelch-repeat proteins differently expressed in Yunong 3114. However, whether
these genes also play any function in grain length regulation in wheat is worth further
investigation.
RLKs in recognition of pollen and high-yield of wheat
A high percentage of genes in the main accumulation profile also attracted our
attentions. One of our most interesting observations was related to genes in the RLK
family that encode receptor-like serine/threonine kinase proteins. The cell surface
RLK family has been reported to play important roles in various physiological
processes, including perceiving external signals [40-42], plant growth and
development [43, 44], plant defense responses against pathogens [45, 46], and so on.
RLKs are classified into different groups according to the large variability of the
extracellular domain organization (over 20 structures exist), such as leucine-rich
repeats, S domains, epidermal growth factor-like repeats, and lectin binding domains
[47, 48]. Previous studies indicated that G-type RLKs function in self-incompatibility
(SI) in flowering plants and plant defense [49-51]. Moreover, a recent research
suggested that over expression of GsSRK in Arabidopsis exhibited enhanced salt
tolerance and higher yields under salt stress, with higher germination rates, higher
chlorophyll content, more green and open leaves, lower ion leakage, higher plant
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
height, and more siliques at the adult developmental stage [52]. This is in accordance
with our results that GsSRK is significantly up-regulated in Yunong 3114. We tried to
find some clues to reveal the underlying relationship. Therefore, the statistics of
locuses of RLK family genes in wheat genome is checked. And we found that 10, 4
and 10 out of 157 unigenes were located on the chromosome 2AL, 2BL and 2DL,
respectively. It is suggested that these genes are only one of the reasons for the high
yield caused by mutations, rather than the position of the mutation. The discovery of
accumulation of receptor-like serine/threonine kinase proteins in Yunong 3114
probably reveals an important involvement with wheat high-yield, which needs
further investigation.
In plants, AS functions in a wide range of processes including development, disease
resistance and stress response [24, 53, 54], especially in the generation of proteomic
and functional diversities [55, 56]. It is reported that serine/arginine-rich proteins, are
extensively alternatively spliced [57]. In our results wheat AS-associated genes
undergo multiple AS events producing a variety of transcripts from a single gene.
These AS events were further classified into seven different types: alternative 3’ splice
sites (A3SS), alternative 5’ splice sites (A5SS), alternative last exons (ALE),
alternative first exons (AFE), mutually exclusive exons (MXE), retained introns (RI),
skipped exons (SE). The different types of AS may reflect underlying differences in
pre-mRNA splicing regulation, affect the protein coding sequence or generate
unproductive mRNAs to affect transcript levels, which further influence the
downstream functions, such as regulating grain weight and length. In our data, RI is
the most frequent AS type, this is consistent with previous reports in Arabidopsis and
Rice [57-59]. However, we found that RI type of AS event ratio is more prevalent in
mutant strain, which may result in different levels of transcripts with wild strain.
Moreover, our results showed that RLK genes significantly up-regulated in mutant
strain, we deducted that these differentially expressed genes in the two genotypes
were due to the fragment deletion on the distal part of transcripts or AS in the mutant
strain, which may increase protein variation and complexity. Interestingly, our AS
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
analysis showed AS event in GsSRK and RLK genes, firmly supporting our
speculation. Furthermore, the AS of these genes probably helps with regulating
transcripts levels illustrating the extremely high complexity of transcriptome
regulation affecting development.
R genes in PCD and defense response may be conducive to high-yield of wheat
Another set of genes we are interested in is involved in PCD and defense response.
PCD process, which programmed eliminated unwanted cells, is an essential process of
development of animals and plants. While in plant cells, it possesses unique
characteristics, such as presence of chloroplasts, a prominent vacuole, and the
cell wall, which affect PCD [60]. It is an essential process occurring throughout the
plant's life cycle, such as grain germination in initial stages of grain development [61,
62], fertilization, xylogenesis, and senescence [63, 64], and also in the accumulation
of storage compounds of starchy endosperm during maturation [65, 66]. Mounting
researches suggested that PCD and disease resistance are complicated linked in plants,
in virtue of the simultaneous activation of disease resistance and cell death upon
pathogen recognition by disease resistance (R) proteins [67]. Plant disease resistance
is often activated by genes with nucleotide binding site (NBS) and leucine-rich repeat
(LRR) or serine/threonine protein kinase (S/TPK) domains [68]. The transcriptome
data show patterns of expression of different genes involved in PCD are similar. We
found numerous genes enriched in PCD category encode disease resistance proteins.
Previous studies have identified that a direct effect of an R gene on yield implies an
underlying mechanistic relationship. A few genes involved in or related to diseases
resistant incurred a yield penalty. However, some others are not. For instance, the 1RS
chromosome arm from rye, carrying several disease resistance genes, is associated
with increased production even in the absence of disease [69]. In our data, we
identified several enriched expressed R genes up-regulated in Yunong 3114 against
Yunong 201, which are possibly critical for in Yunong 3114. As indicated previously,
the up-regulated R genes in Yunong 3114 would reinforce the ability of disease
resistance, more importantly, they may increase yield even during no disease
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
conditions. Unfortunately, we found that the R genes were distributed in each
chromosome with no obvious distribution characteristics. Furthermore, many reports
suggest that alternative transcripts may limit the expression of R proteins or encode
truncated R proteins with a positive role in defense activation. In addition, R gene
alternative splicing is dynamic during the defense response. Thus in our study, we also
analyzed AS of R genes as well as RLKs. Interesting, different from RLKs, we
detected no AS in R genes. This is probably due to incomplete annotations. However,
the further investigation will be addressed in the further studies.
In summary, in this work, we performed a transcriptome sequencing of a new mutant
wheat strain Yunong 3114. The functional annotation of the transcriptomes provide a
further insight into the gene content, biological processes, molecular functions and
pathways conserved in wheat. By comparing with its common wheat strain Yunong
201, we found several enriched GO terms and genes probably associated with
high-yield phenotype of Yunong 3114, which are worthy discovered to improve yield
for increased food or feed productivity. Our data and results provide essential
information for future genetic improvements and management in increase-yield and
breeding at the transcriptome level.
Acknowledgments
This project was funded by the National Natural Science Foundation (31370031),
973 project (2014CB138105) and Program for New Century Excellent Talents in
University (NCET-13-0776) of China.
F.C. and D.C. designed the project and performed RNA-seq experiments. Z.D., N.Z.
and X.Z. performed the computational analyses. F.C. wrote the paper.
References
1.
Cordain, L.,
Diet, 1999.
2.
Food
Cereal grains: humanity's double-edged sword.
World Rev Nutr
84: p. 19-73.
and
Agriculture
Organization
of
the
United
Nations.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
[http://faostat.fao.org], 2011. figures.
3.
Tester,
M.
and
P.
Langridge,
Breeding technologies to increase crop
production in a changing world. Science, 2010. 327(5967): p. 818-22.
4.
Schreiber,
A.W.,
et
al.,
Transcriptome-scale homoeolog-specific transcript
assemblies of bread wheat. BMC Genomics, 2012. 13: p. 492.
5.
Tasleem-Tahir, A., et al.,
Proteomic analysis of peripheral layers during wheat
(Triticum aestivum L.) grain development. Proteomics, 2011. 11(3): p. 371-9.
6.
Gao, F., M.C. Jordan, and B.T. Ayele,
Transcriptional programs regulating seed
dormancy and its release by after-ripening in common wheat (Triticum
aestivum L.). Plant Biotechnol J, 2012. 10(4): p. 465-76.
7.
Liu,
Y., et al.,
The Relationship between Polyamines and Hormones in the
Regulation of Wheat Grain Filling. PLoS One, 2013. 8(10): p. e78196.
8.
Berkman, P.J., et al.,
Next-generation sequencing applications for wheat crop
improvement. Am J Bot, 2012. 99(2): p. 365-71.
9.
Brenchley,
R.,
et
al.,
Analysis of the bread wheat genome using
whole-genome shotgun sequencing. Nature, 2012. 491(7426): p. 705-10.
10.
Wan,
Y., et al.,
Transcriptome analysis of grain development in hexaploid
wheat. BMC Genomics, 2008. 9: p. 121.
11.
Xiao, J., et al.,
Transcriptome-based discovery of pathways and genes related
to resistance against Fusarium head blight in wheat landrace Wangshuibai.
BMC Genomics, 2013.
12.
Krasileva,
K.V.,
et
14: p. 197.
al.,
Separating homeologs by phasing in the tetraploid
wheat transcriptome. Genome Biol, 2013. 14(6): p. R66.
13.
Garnica, D.P., et al.,
Strategies for Wheat Stripe Rust Pathogenicity Identified
by Transcriptome Sequencing. PLoS One, 2013. 8(6): p. e67150.
14.
Xin, M., et al.,
in
response
Transcriptome comparison of susceptible and resistant wheat
to
powdery
Bioinformatics, 2012.
15.
mildew
infection.
Genomics
Proteomics
10(2): p. 94-106.
Tenea, G.N., F. Cordeiro Raposo, and A. Maquet,
Comparative transcriptome
profiling in winter wheat grown under different agricultural practices. J Agric
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Food Chem, 2012.
16.
Winfield,
M.O.,
60(44): p. 10970-8.
et
al.,
Plant responses to cold: Transcriptome analysis of
wheat. Plant Biotechnol J, 2010. 8(7): p. 749-71.
17.
Spannagl, M., et al.,
Analysing complex Triticeae genomes - concepts and
strategies. Plant Methods, 2013. 9(1): p. 35.
18.
Kumar,
A.,
et
al.,
Physical mapping resources for large plant genomes:
radiation hybrids for wheat D-genome progenitor Aegilops tauschii.
Genomics, 2012.
19.
Li, W., et al.,
BMC
13: p. 597.
Sequence composition, organization, and evolution of the core
Triticeae genome. Plant J, 2004. 40(4): p. 500-11.
20.
Tanaka,
T.,
et
al.,
Next-Generation Survey Sequencing and the Molecular
Organization of Wheat Chromosome 6B. DNA Res, 2013.
21.
Garg, R., et al.,
Gene discovery and tissue-specific transcriptome analysis in
chickpea with massively parallel pyrosequencing and web resource
development. Plant Physiol, 2011. 156(4): p. 1661-78.
22.
Duan, J., et al.,
Optimizing de novo common wheat transcriptome assembly
using short-read RNA-Seq data. BMC Genomics, 2012. 13: p. 392.
23.
Oono,
Y.,
et
al.,
Characterisation of the wheat (Triticum aestivum L.)
transcriptome by de novo assembly for the discovery of phosphate
starvation-responsive genes: gene expression in Pi-stressed wheat.
Genomics, 2013.
24.
Blencowe, B.J.,
2006.
25.
BMC
14: p. 77.
Alternative splicing: new insights from global analyses.
Cell,
126(1): p. 37-47.
Ling, H.Q., et al.,
Draft genome of the wheat A-genome progenitor Triticum
urartu. Nature, 2013. 496(7443): p. 87-90.
26.
Kuriyama, H. and H. Fukuda,
Curr Opin Plant Biol, 2002.
27.
Watanabe,
N.
and
E.
Developmental programmed cell death in plants.
5(6): p. 568-73.
Lam,
Recent advance in the study of caspase-like
proteases and Bax inhibitor-1 in plants: their possible roles as regulator of
programmed cell death. Mol Plant Pathol, 2004. 5(1): p. 65-70.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
28.
Black, D.L.,
Mechanisms of alternative pre-messenger RNA splicing. Annu Rev
Biochem, 2003.
29.
Matlin,
A.J.,
F.
72: p. 291-336.
Clark,
and
C.W.
Smith,
Understanding alternative splicing:
towards a cellular code. Nat Rev Mol Cell Biol, 2005. 6(5): p. 386-98.
30.
Reddy,
A.S.,
Alternative splicing of pre-messenger RNAs in plants in the
genomic era. Annu Rev Plant Biol, 2007. 58: p. 267-94.
31.
Saintenac,
C.,
et
al.,
Sequence-based mapping of the polyploid wheat
genome. G3 (Bethesda), 2013. 3(7): p. 1105-14.
32.
Nagaraj, S.H., et al.,
ESTExplorer: an expressed sequence tag (EST) assembly
and annotation platform.
Nucleic Acids Res, 2007.
35(Web
Server issue): p.
W143-7.
33.
Bouck,
A.,
S.R.
Wessler,
and
M.L.
Arnold,
QTL analysis of floral traits in
Louisiana iris hybrids. Evolution, 2007. 61(10): p. 2308-19.
34.
Kota, R., et al.,
Snipping polymorphisms from large EST collections in barley
(Hordeum vulgare L.). Mol Genet Genomics, 2003. 270(1): p. 24-33.
35.
Garg,
R.,
et
Deep Transcriptome Sequencing of Wild Halophyte Rice,
al.,
Porteresia coarctata, Provides Novel Insights into the Salinity and
Submergence Tolerance Factors. DNA Res, 2013.
36.
Martin, G.B., A.J. Bogdanove, and G. Sessa,
Understanding the functions of
plant disease resistance proteins. Annu Rev Plant Biol, 2003. 54: p. 23-61.
37.
Zhang, X., et al.,
Rare allele of OsPPKL1 associated with grain length causes
extra-large grain and a significant yield increase in rice. Proc Natl Acad Sci U S
A, 2012.
38.
109(52): p. 21534-9.
Song, X.J., et al.,
A QTL for rice grain width and weight encodes a previously
unknown RING-type E3 ubiquitin ligase. Nat Genet, 2007. 39(5): p. 623-30.
39.
Fan, C., et al.,
GS3, a major QTL for grain length and weight and minor QTL for
grain width and thickness in rice, encodes a putative transmembrane protein.
Theor Appl Genet, 2006.
40.
Stone,
J.M.
and
J.C.
112(6): p. 1164-71.
Walker,
Plant protein kinase families and signal
transduction. Plant Physiol, 1995. 108(2): p. 451-7.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
41.
Lease,
K.,
E.
Ingham,
and
J.C.
Walker,
Challenges in understanding RLK
function. Curr Opin Plant Biol, 1998. 1(5): p. 388-92.
42.
Genome-wide identification, characterization and expression
Zan, Y., et al.,
analysis of populus leucine-rich repeat receptor-like protein kinase genes.
BMC Genomics, 2013.
43.
Hematy,
K.
and
H.
14: p. 318.
Hofte,
Novel receptor kinases involved in growth
regulation. Curr Opin Plant Biol, 2008. 11(3): p. 321-8.
44.
Nibau, C. and A.Y. Cheung,
New insights into the functional roles of CrRLKs in
the control of plant cell growth and development. Plant
Signal Behav, 2011.
6(5): p. 655-9.
45.
Ederli, L., et al.,
The Arabidopsis thaliana cysteine-rich receptor-like kinase
CRK20 modulates host responses to Pseudomonas syringae pv. tomato
DC3000 infection. J Plant Physiol, 2011. 168(15): p. 1784-94.
46.
Yang, Y., et al.,
Proteomic study participating the enhancement of growth and
salt tolerance of bottle gourd rootstock-grafted watermelon seedlings. Plant
Physiol Biochem, 2012.
47.
Zhao,
J.,
et
al.,
58: p. 54-65.
A receptor-like kinase gene (GbRLK) from Gossypium
barbadense enhances salinity and drought-stress tolerance in Arabidopsis.
BMC Plant Biol, 2013.
48.
13: p. 110.
Shiu, S.H. and A.B. Bleecker,
Receptor-like kinases from Arabidopsis form a
monophyletic gene family related to animal receptor kinases. Proc Natl Acad
Sci U S A, 2001.
49.
98(19): p. 10763-8.
Sherman-Broyles, S., et al.,
S locus genes and the evolution of self-fertility in
Arabidopsis thaliana. Plant Cell, 2007. 19(1): p. 94-106.
50.
Kim, H.S., et al.,
An S-locus receptor-like kinase plays a role as a negative
regulator in plant defense responses.
Biochem Biophys Res Commun, 2009.
381(3): p. 424-8.
51.
Kim, S., et al.,
An atypical soybean leucine-rich repeat receptor-like kinase,
GmLRK1, may be involved in the regulation of cell elongation. Planta,
229(4): p. 811-21.
2009.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
52.
Sun, X.L., et al.,
GsSRK, a G-type lectin S-receptor-like serine/threonine protein
kinase, is a positive regulator of plant tolerance to salt stress. J Plant Physiol,
2013.
53.
170(5): p. 505-15.
Macknight,
R.,
et
al.,
Functional significance of the alternative transcript
processing of the Arabidopsis floral promoter FCA. Plant Cell, 2002. 14(4): p.
877-88.
54.
Gassmann,
Immunol, 2008.
55.
Alternative splicing in plant defense.
W.,
Curr
Top
Microbiol
326: p. 219-33.
Zhang, X.C. and W. Gassmann,
Alternative splicing and mRNA levels of the
disease resistance gene RPS4 are induced during defense responses.
Physiol, 2007.
56.
Guo,
S.,
et
Plant
145(4): p. 1577-87.
al.,
Transcriptome sequencing and comparative analysis of
cucumber flowers with different sex types. BMC Genomics, 2010. 11: p. 384.
57.
Palusa, S.G., G.S. Ali, and A.S. Reddy,
Alternative splicing of pre-mRNAs of
Arabidopsis serine/arginine-rich proteins: regulation by hormones and
stresses. Plant J, 2007. 49(6): p. 1091-107.
58.
Zhang, G., et al.,
Deep RNA sequencing at single base-pair resolution reveals
high complexity of the rice transcriptome.
Genome
Res,
2010.
20(5):
p.
646-54.
59.
E,
Z.,
L.
Wang,
and
J.
Zhou,
Splicing and alternative splicing in rice and
humans. BMB Rep, 2013. 46(9): p. 439-47.
60.
Williams, B. and M. Dickman,
Plant programmed cell death: can't live with it;
can't live without it. Mol Plant Pathol, 2008. 9(4): p. 531-44.
61.
Dominguez, F. and F.J. Cejudo,
Identification of a nuclear-localized nuclease
from wheat cells undergoing programmed cell death that is able to trigger
DNA fragmentation and apoptotic morphology on nuclei from human cells.
Biochem J, 2006.
62.
397(3): p. 529-36.
Dominguez, F., J. Moreno, and F.J. Cejudo,
The nucellus degenerates by a
process of programmed cell death during the early stages of wheat grain
development. Planta, 2001. 213(3): p. 352-60.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
63.
Greenberg, J.T.,
Programmed cell death: a way of life for plants.
Acad Sci U S A, 1996.
64.
Bai, S., et al.,
Proc Natl
93(22): p. 12094-7.
Proteomic analysis of pollination-induced corolla senescence in
petunia. J Exp Bot, 2010. 61(4): p. 1089-109.
65.
Young, T.E., D.R. Gallie, and D.A. DeMason,
Ethylene-Mediated Programmed
Cell Death during Maize Endosperm Development of Wild-Type and
shrunken2 Genotypes. Plant Physiol, 1997. 115(2): p. 737-751.
66.
Young,
T.E.
and
D.R.
Gallie,
Programmed cell death during endosperm
development. Plant Mol Biol, 2000. 44(3): p. 283-301.
67.
Yang, H., et al.,
The Arabidopsis BAP1 and BAP2 genes are general inhibitors
of programmed cell death. Plant Physiol, 2007. 145(1): p. 135-46.
68.
Faris,
J.D.,
et
al.,
A unique wheat disease resistance-like gene governs
effector-triggered susceptibility to necrotrophic pathogens. Proc Natl Acad Sci
U S A, 2010.
69.
Brown, J.K.,
2002.
70.
107(30): p. 13544-9.
Yield penalties of disease resistance in crops. Curr Opin Plant Biol,
5(4): p. 339-44.
Chen, F., Gao, M.X., Zhang, J.H., Zuo, A.H., Shang, X.L., Cui, D.Q. Molecular
characterization of vernalization and response genes in bread wheat from the
Yellow and Huai valley of China. BMC Plant Biology 2013, 13:199
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Figure legends
Figure 1. RPKM distribution of Unigenes.
Figure 2. NR annotation of Unigenes. The proportion of Unigenes with matches in
NR is exhibited by different colors and annotated beside.
Figure 3. Clusters of orthologous groups (COG) functional classification of Unigenes.
29,721 Unigenes had a COG classification. The y-axis indicates the number of genes
in a category. The x-axis indicates the categories of COG functions, represented by
capital English characters with annotation on the right side.
Figure 4. Gene Ontology classification of Unigenes. 89,644 Unigenes were
categorized into 64 functional groups of three main categories: biological process,
cellular component and molecular function. The y-axis indicates the number of genes
in a category.
Figure 5. Heat map. The clusters generated based on similarities in expression profile
of probe sets differentially expressed in the samples. The red represents samples from
wild strain Yunong 201; while the green shows mutant strain Yunong 3114.
Figure 6. Venn diagram comparison of differentially expressed genes (DEGs) in
enrichment GO term of recognition of pollen in Yunong 3114 and 201. The pink cycle
in the left represents genes enriched in the category of recognition of pollen; the blue
cycle within shows genes of up-regulation and down-regulation. The up or down
arrows indicated that DEGs were up- or down-regulated; The red and green ovals
show proteins encoded by up- and down-regulated DEGs in the category with
annotations in Swissprot, which are listed in the right tables. a) shows proteins only
encoded by up-regulated genes; b) proteins encoded by both up- and down-regulated
genes; c) proteins encoded by down-regulated genes.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Figure 7. Venn diagram comparison of DEGs in PCD and defense response in Yunong
3114 and 201. The upper cycles represent DEGs enriched in PCD and defense
response; the lower cycles show proteins encoded by DEGs in PCD and defense
response with annotations in Swissprot. The representation of each cycle is marked by
blue arrows.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Table legends
Table 1. Sequencing and draft transcriptome assembly statistics.
All
>=500
>=1000
(>=200bp)
bp
bp
N50
N90
Total
Max
Min
Average
Length
Length
Length
Length
Transcript
743086
414871
246268
1516
382
695863802
14748
201
936.45
Unigene
289681
89444
42109
886
253
172782515
14748
201
596.46
The N50 is defined as the length of the contig that spans the median length of the full assembly.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Table 2. Sequencing transcriptome assembly statistics.
All
>=100bp
>=500
>=1000
bp
bp
N50
N90
Total
Max
Min
Average
Length
Length
Length
Length
Transcript
768141
762662
422627
247246
1498
381
705722361
14748
50
918.74
Unigene
422144
418641
188538
99839
1267
306
322725389
14748
50
764.49
The N50 is defined as the length of the contig that spans the median length of the full assembly.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Table 3. RPKM of Unigenes.
Sample
Expunigene
Min.
1st
Median
Mean
Qu.
3rd
Max.
Sd.
Sum.
Qu.
201_1
325401
0
0.19
0.42
2.43
1.05
1836.87
16.09
795839.1
201_2
282383
0
0.13
0.29
3.53
0.69
31487.75
157.27
1002426
201_3
288840
0
0.18
0.39
2.69
0.97
6234.93
31.51
781860.5
201_4
291000
0
0.16
0.36
2.61
0.91
7751.11
35.43
764622.8
201_5
315519
0
0.2
0.42
2.44
1.01
12583.74
42.46
771698.8
201_6
282012
0
0.09
0.26
2.61
0.73
29448.49
87.47
743982.2
3114_1
313387
0
0.2
0.45
2.52
1.18
1523.99
15.38
792081.5
3114_2
282311
0
0.13
0.3
3.47
0.7
29372.78
139.14
983734.6
3114_3
304875
0
0.17
0.36
2.49
0.92
10054.1
33.18
761252.7
3114_4
285307
0
0.21
0.44
2.67
1.03
10669.99
37.01
764567.6
3114_5
317360
0
0.19
0.4
2.32
0.97
9904.3
31.94
738537.8
3114_6
299595
0
0.14
0.35
2.39
0.85
28737.8
65.63
721550.8
All
414330
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Table 4. The network of Unigenes in KEGG database.
43,501 Unigenes were assigned to 313 KEGG pathways and the top five representative networks were
listed.
Pathway
Pathway_type
Unigene number
Metabolism/Carbohydrate Metabolism
1855
Metabolism/Energy Metabolism
1767
ko03010, Ribosome
Genetic Information Processing/Translation
1309
ko03040, Spliceosome
Genetic Information Processing/Transcription
1165
Organismal Systems/Environmental Adaptation
1130
ko00630,
Glyoxylate
and
dicarboxylate metabolism
ko00710, Carbon fixation in
photosynthetic organisms
ko04626,
interaction
Plant-pathogen
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Table 5. Enriched differently expressed GO terms.
Go terms
Up-regulated
Name
Molecular Function
Unigenes
Number
P value
201_1 VS
201_2VS31
201_3VS31
201_4VS31
201_5VS31
201_6VS31
3114_1
14_2
14_3
14_4
14_5
14_6
GO:0004872
receptor activity
834
7.45E-29
3.40E-26
5.92E-17
2.56E-22
3.79E-14
3.60E-09
GO:0004518
nuclease activity
600
1.24E-17
6.38E-09
7.68E-11
7.46E-11
4.23E-08
7.44E-08
GO:0003964
RNA-directed DNA polymerase
2108
1.37E-251
1.63E-157
1.24E-191
3.75E-206
6.38E-93
5.21E-52
endonuclease activity
579
2.70E-28
6.99E-17
1.86E-22
2.20E-18
1.64E-14
4.27E-15
GO:0016779
nucleotidyltransferase activity
848
1.86E-23
6.04E-17
7.36E-15
4.94E-16
1.72E-14
4.77E-11
GO:0016772
transferase activity, transferring
5640
2.22E-55
6.15E-31
3.76E-33
4.86E-21
0.33070462
1.13E-07
activity
GO:0004519
phosphorus-containing groups
GO:0004674
protein serine/threonine kinase
8
4646
1.09E-75
1.11E-42
1.37E-47
3.49E-34
activity
0.03698683
0.00058391
7
GO:0004672
protein kinase activity
5332
1.00E-66
2.60E-38
2.46E-42
7.37E-30
0.17323454
0.00019764
4
9
GO:0005524
ATP binding
12503
3.60E-55
1.38E-42
3.17E-34
1.07E-19
0.02324696
3.38E-26
GO:0000166
nucleotide binding
9669
4.64E-07
0.00421672
0.00167100
0.99508287
0.99304205
0.07341437
2
6
2
2
1
Biological Process
GO:0006952
defense response
3030
1.25E-126
4.83E-120
1.76E-106
2.03E-84
1.62E-21
3.39E-51
GO:0048544
recognition of pollen
380
1.23E-34
1.77E-28
1.33E-41
8.09E-24
3.61E-13
2.27E-12
GO:0015074
DNA integration
1410
8.70E-157
5.54E-90
6.47E-135
1.63E-112
1.02E-44
2.98E-30
GO:0006468
protein phosphorylation
5340
1.76E-66
3.48E-38
3.11E-42
1.22E-29
0.18091960
0.00016376
7
7
GO:0006915
apoptotic process
2755
1.15E-106
1.36E-129
5.88E-103
6.34E-92
1.00E-31
1.58E-41
GO:0006278
RNA-dependent DNA replication
1734
1.19E-205
4.56E-122
6.04E-152
1.16E-166
2.10E-67
1.10E-37
1094
9.39E-19
3.98E-26
4.41E-32
2.81E-20
4.09E-05
0.54640348
Down-regulated
GO:0016705
Molecular Function
oxidoreductase activity, acting on
paired donors, with incorporation
1
or reduction of molecular oxygen
GO:0004872
receptor activity
834
7.45E-29
3.40E-26
5.92E-17
2.56E-22
3.79E-14
3.60E-09
GO:0004499
N,N-dimethylanilinemonooxygen
157
0.00490610
6.17E-07
1.27E-11
0.00044643
7.31E-11
0.01127677
GO:0004553
hydrolase activity, hydrolyzing
1448
3.91E-10
7.42E-09
4.49E-14
4.80E-10
2.32E-56
5.47E-11
255
0.00380342
0.00163577
2.83E-06
7.59E-06
4.34E-07
0.0008893
ase activity
9
2
5
O-glycosyl compounds
GO:0005507
copper ion binding
2
GO:0005506
iron ion binding
1814
1.56E-24
7.23E-29
5.98E-46
1.55E-26
2.09E-12
0.32402504
GO:0016798
hydrolase activity, acting on
1204
2.95E-08
1.06E-13
1.92E-06
1.58E-07
2.72E-41
6.66E-12
195
4.07E-37
0.00059038
0.00025865
2.85E-08
2.65E-05
3.78E-10
glycosyl bonds
GO:0030599
pectinesterase activity
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
9
1
GO:0004857
enzyme inhibitor activity
353
1.02E-30
6.36E-07
1.51E-08
5.30E-15
1.23E-09
1.67E-09
GO:0042802
identical protein binding
255
1.33E-07
6.99E-08
5.80E-08
6.21E-12
5.16E-05
1.16E-05
GO:0005215
transporter activity
1280
3.23E-14
8.16E-12
1.16E-08
9.78E-09
1.56E-19
9.86E-08
GO:0008233
peptidase activity
1704
7.85E-15
1.42E-07
6.92E-07
1.64E-08
3.62E-13
5.73E-11
GO:0020037
heme binding
1701
1.41E-25
9.20E-33
4.87E-40
1.60E-44
1.56E-17
0.00018081
6
GO:0009055
electron carrier activity
1796
1.32E-14
8.41E-20
1.26E-36
1.05E-21
9.06E-11
0.35402591
9
Biological Process
GO:0048544
recognition of pollen
380
1.23E-34
1.77E-28
1.33E-41
8.09E-24
3.61E-13
2.27E-12
GO:0043086
negative regulation of catalytic
371
4.17E-34
2.80E-16
1.41E-19
4.27E-30
1.70E-16
9.76E-13
1884
3.23E-48
8.37E-26
8.36E-31
1.14E-51
2.35E-62
5.53E-40
activity
Cell Component
GO:0005576
extracellular region
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Table 6. AS events and alternatively spliced genes in wheat Yunong 201 and Yunong 3114.
Yunong 201
Yunong 3114
AS type
Gene (ratio)
Event (ratio)
Gene (ratio)
Event (ratio)
A3SS
347 (7.00%)
500 (4.34%)
301 (5.17%)
371 (2.78%)
A5SS
208 (4.19%)
265 (2.30%)
120 (2.06%)
139 (1.04%)
AFE
65 (1.31%)
110 (0.95%)
43 (0.74%)
71 (0.53%)
ALE
67 (1.35%)
100 (0.87%)
43 (0.74%)
55 (0.41%)
MXE
18 (0.36%)
38 (0.33%)
23 (0.40%)
34 (0.26%)
RI
3823 (77.08%)
9860 (85.58%)
5038 (86.59%)
12360 (92.72%)
SE
432 (8.71%)
648 (5.62%)
250 (4.30%)
301 (2.26%)
Total
4960
11521
5818
13331
AS: alternative splicing; A3SS: alternative 3’ splice sites; A5SS: alternative 5’ splice sites; ALE:
alternative last exons; AFE: alternative first exons; MXE: mutually exclusive exons; RI: retained
introns; SE: skipped exons.
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.
Table 7. AS events of pollen related genes in wild and mutant strains.
RefgeneID
Location
Constitutive
Alternative
Constitutive
(strand)
exon
exon
exon
TRIUR3_25
scaffold41
23440-23547
23440-23550
23667-23999
324
665 (-)
Unigene
AS
Strain
Length
Swissprot_des
Yunong
1273
sp|O81906|B120_ARATH
type
comp19669
A3SS
9_c0_seq1
201
G-type lectin
(wild)
S-receptor-like
serine/threonine-protein
kinase B120
TRIUR3_24
scaffold76
895
660 (-)
21964-22275
22276-22358
22359-22509
comp21116
RI
9_c0_seq10
Yunong
1641
sp|Q39086|SD17_ARATH
201
Receptor-like
(wild)
serine/threonine-protein
kinase SD1-7
TRIUR3_24
scaffold76
895
660 (-)
22595-22835
22836-22911
22912-23122
comp21116
RI
9_c0_seq10
Yunong
1641
sp|Q39086|SD17_ARATH
3114
Receptor-like
(mutant)
serine/threonine-protein
kinase SD1-7
TRIUR3_02
scaffold96
437
181 (-)
38493-39672
39673-39749
39750-39816
comp18089
7_c0_seq2
RI
Yunong
3114
(mutant)
3045
NA
bioRxiv preprint first posted online May 27, 2014; doi: http://dx.doi.org/10.1101/005496; The copyright holder for
this preprint is the author/funder. It is made available under a CC-BY-NC 4.0 International license.

Download Report